Ashok Prakash is a technical architect who designs, builds, and operates high-performance cloud platforms for GPU-accelerated AI/ML and HPC workloads. His work centers on turning complex, multi-tenant infrastructure into predictable, self-service platforms that engineering teams can adopt without friction. At Oracle Cloud (Senior Principal Engineer, 2020–present), Ashok led platform engineering efforts to deliver a Kubernetes-based GPU platform that abstracts bare-metal clusters behind self-service APIs. The result: product teams can bring up AI workloads in minutes instead of days, with consistent provisioning, quota controls, and cost visibility. He has built and evolved multi-tenant schedulers for GPU/CPU pools, automated cluster lifecycle, and developer tooling that bakes in “good defaults” for observability, CI/CD, and runtime security.

His area of expertise extends beyond infrastructure plumbing into the day-to-day productivity of engineering organizations. He partners with data and platform teams on reproducible pipelines, tracing/metrics that actually get used, and incident practices that prefer prevention over heroics. In reviews and mentoring, he emphasizes steady delivery over theatrics and favors measurable, team-owned improvements, things like time-to-first-draft for docs and tests, human approval rates for AI-assisted changes, and SLOs that reflect real user experience.

Ashok speaks regularly at engineering and data conferences, including Airflow Summit, where he shares vendor-neutral patterns for using AI safely in everyday engineering work. Typical topics include turning code comments into first-draft documentation, generating unit-test scaffolds, suggesting refactors with human review, writing clearer triage notes, and deciding what remains human-only (novel architecture, policy exceptions, high-impact incident calls).

He holds three published U.S. patent applications (pending issuance) and international publications in AI/NLP, with citations from both academic and industry readers. His writing and talks frequently return to a few core ideas: pave the path so the right thing is the easy thing; keep sensitive data out of public tools; measure outcomes users can feel; and favor small, repeatable wins over sweeping rewrites.

Ashok is based in Seattle. Outside of day job responsibilities, he contributes practical guidance on AI-at-work for non-specialists, helping teams adopt simple prompt patterns, basic data-hygiene rules, and review gates that keep judgment with humans. He is the author of Human Reimagined: Thriving in the Age of AI, a non-technical guide for professionals who want to make AI a helpful assistant rather than a source of anxiety.